Overview

Dataset statistics

Number of variables28
Number of observations2051
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory529.2 KiB
Average record size in memory264.2 B

Variable types

Categorical12
Numeric16

Alerts

Income is highly overall correlated with Is_Parent and 11 other fieldsHigh correlation
Is_Parent is highly overall correlated with Income and 3 other fieldsHigh correlation
Memebers_In_Family is highly overall correlated with Is_Parent and 1 other fieldsHigh correlation
MntFishProducts is highly overall correlated with Income and 8 other fieldsHigh correlation
MntFruits is highly overall correlated with Income and 8 other fieldsHigh correlation
MntGoldProds is highly overall correlated with MntFishProducts and 9 other fieldsHigh correlation
MntMeatProducts is highly overall correlated with Income and 10 other fieldsHigh correlation
MntSweetProducts is highly overall correlated with Income and 8 other fieldsHigh correlation
MntWines is highly overall correlated with Income and 10 other fieldsHigh correlation
No_of_Childrens is highly overall correlated with Is_Parent and 2 other fieldsHigh correlation
NumCatalogPurchases is highly overall correlated with Income and 11 other fieldsHigh correlation
NumDealsPurchases is highly overall correlated with Is_Parent and 1 other fieldsHigh correlation
NumStorePurchases is highly overall correlated with Income and 10 other fieldsHigh correlation
NumWebPurchases is highly overall correlated with Income and 7 other fieldsHigh correlation
NumWebVisitsMonth is highly overall correlated with Income and 1 other fieldsHigh correlation
Total_Amnt_Spend is highly overall correlated with Income and 10 other fieldsHigh correlation
Total_Purchase is highly overall correlated with Income and 7 other fieldsHigh correlation
AcceptedCmp3 is highly imbalanced (62.2%)Imbalance
AcceptedCmp4 is highly imbalanced (60.8%)Imbalance
AcceptedCmp5 is highly imbalanced (62.8%)Imbalance
AcceptedCmp1 is highly imbalanced (65.2%)Imbalance
AcceptedCmp2 is highly imbalanced (90.2%)Imbalance
Recency has 26 (1.3%) zerosZeros
MntFruits has 367 (17.9%) zerosZeros
MntFishProducts has 358 (17.5%) zerosZeros
MntSweetProducts has 385 (18.8%) zerosZeros
MntGoldProds has 57 (2.8%) zerosZeros
NumDealsPurchases has 42 (2.0%) zerosZeros
NumWebPurchases has 45 (2.2%) zerosZeros
NumCatalogPurchases has 536 (26.1%) zerosZeros

Reproduction

Analysis started2024-12-18 13:21:20.612186
Analysis finished2024-12-18 13:22:17.752098
Duration57.14 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Education
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size96.6 KiB
Graduation
1028 
PhD
447 
Master
339 
2n Cycle
188 
Basic
 
49

Length

Max length10
Median length10
Mean length7.5104827
Min length3

Characters and Unicode

Total characters15404
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGraduation
2nd rowGraduation
3rd rowGraduation
4th rowGraduation
5th rowPhD

Common Values

ValueCountFrequency (%)
Graduation 1028
50.1%
PhD 447
21.8%
Master 339
 
16.5%
2n Cycle 188
 
9.2%
Basic 49
 
2.4%

Length

2024-12-18T18:52:17.924300image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T18:52:18.169800image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
graduation 1028
45.9%
phd 447
20.0%
master 339
 
15.1%
2n 188
 
8.4%
cycle 188
 
8.4%
basic 49
 
2.2%

Most occurring characters

ValueCountFrequency (%)
a 2444
15.9%
r 1367
8.9%
t 1367
8.9%
n 1216
 
7.9%
i 1077
 
7.0%
G 1028
 
6.7%
d 1028
 
6.7%
u 1028
 
6.7%
o 1028
 
6.7%
e 527
 
3.4%
Other values (12) 3294
21.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12530
81.3%
Uppercase Letter 2498
 
16.2%
Decimal Number 188
 
1.2%
Space Separator 188
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2444
19.5%
r 1367
10.9%
t 1367
10.9%
n 1216
9.7%
i 1077
8.6%
d 1028
8.2%
u 1028
8.2%
o 1028
8.2%
e 527
 
4.2%
h 447
 
3.6%
Other values (4) 1001
8.0%
Uppercase Letter
ValueCountFrequency (%)
G 1028
41.2%
D 447
17.9%
P 447
17.9%
M 339
 
13.6%
C 188
 
7.5%
B 49
 
2.0%
Decimal Number
ValueCountFrequency (%)
2 188
100.0%
Space Separator
ValueCountFrequency (%)
188
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15028
97.6%
Common 376
 
2.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2444
16.3%
r 1367
9.1%
t 1367
9.1%
n 1216
8.1%
i 1077
 
7.2%
G 1028
 
6.8%
d 1028
 
6.8%
u 1028
 
6.8%
o 1028
 
6.8%
e 527
 
3.5%
Other values (10) 2918
19.4%
Common
ValueCountFrequency (%)
2 188
50.0%
188
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15404
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2444
15.9%
r 1367
8.9%
t 1367
8.9%
n 1216
 
7.9%
i 1077
 
7.0%
G 1028
 
6.7%
d 1028
 
6.7%
u 1028
 
6.7%
o 1028
 
6.7%
e 527
 
3.4%
Other values (12) 3294
21.4%

Marital_Status
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size96.6 KiB
Single
1256 
Married
795 

Length

Max length7
Median length6
Mean length6.3876158
Min length6

Characters and Unicode

Total characters13101
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowSingle
3rd rowSingle
4th rowSingle
5th rowMarried

Common Values

ValueCountFrequency (%)
Single 1256
61.2%
Married 795
38.8%

Length

2024-12-18T18:52:18.388281image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T18:52:18.567642image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
single 1256
61.2%
married 795
38.8%

Most occurring characters

ValueCountFrequency (%)
i 2051
15.7%
e 2051
15.7%
r 1590
12.1%
S 1256
9.6%
n 1256
9.6%
g 1256
9.6%
l 1256
9.6%
M 795
 
6.1%
a 795
 
6.1%
d 795
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11050
84.3%
Uppercase Letter 2051
 
15.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 2051
18.6%
e 2051
18.6%
r 1590
14.4%
n 1256
11.4%
g 1256
11.4%
l 1256
11.4%
a 795
 
7.2%
d 795
 
7.2%
Uppercase Letter
ValueCountFrequency (%)
S 1256
61.2%
M 795
38.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 13101
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 2051
15.7%
e 2051
15.7%
r 1590
12.1%
S 1256
9.6%
n 1256
9.6%
g 1256
9.6%
l 1256
9.6%
M 795
 
6.1%
a 795
 
6.1%
d 795
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13101
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 2051
15.7%
e 2051
15.7%
r 1590
12.1%
S 1256
9.6%
n 1256
9.6%
g 1256
9.6%
l 1256
9.6%
M 795
 
6.1%
a 795
 
6.1%
d 795
 
6.1%

Income
Real number (ℝ)

HIGH CORRELATION 

Distinct1973
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52354.643
Minimum1730
Maximum666666
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.6 KiB
2024-12-18T18:52:18.777474image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1730
5-th percentile19047.5
Q135694.5
median52074
Q368277.5
95-th percentile84017
Maximum666666
Range664936
Interquartile range (IQR)32583

Descriptive statistics

Standard deviation25405.447
Coefficient of variation (CV)0.48525681
Kurtosis166.22582
Mean52354.643
Median Absolute Deviation (MAD)16309
Skewness7.1005635
Sum1.0737937 × 108
Variance6.4543675 × 108
MonotonicityNot monotonic
2024-12-18T18:52:19.040558image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52357.79154 24
 
1.2%
7500 12
 
0.6%
47025 3
 
0.1%
83151 2
 
0.1%
40049 2
 
0.1%
57091 2
 
0.1%
65104 2
 
0.1%
57100 2
 
0.1%
43776 2
 
0.1%
54809 2
 
0.1%
Other values (1963) 1998
97.4%
ValueCountFrequency (%)
1730 1
< 0.1%
2447 1
< 0.1%
3502 1
< 0.1%
4023 1
< 0.1%
4428 1
< 0.1%
4861 1
< 0.1%
5305 1
< 0.1%
5648 1
< 0.1%
6560 1
< 0.1%
6835 1
< 0.1%
ValueCountFrequency (%)
666666 1
< 0.1%
162397 1
< 0.1%
160803 1
< 0.1%
157733 1
< 0.1%
157243 1
< 0.1%
157146 1
< 0.1%
156924 1
< 0.1%
153924 1
< 0.1%
113734 1
< 0.1%
105471 1
< 0.1%

Recency
Real number (ℝ)

ZEROS 

Distinct100
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.01999
Minimum0
Maximum99
Zeros26
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size96.6 KiB
2024-12-18T18:52:19.354509image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.5
Q124
median49
Q374
95-th percentile94
Maximum99
Range99
Interquartile range (IQR)50

Descriptive statistics

Standard deviation28.999312
Coefficient of variation (CV)0.59158135
Kurtosis-1.2077388
Mean49.01999
Median Absolute Deviation (MAD)25
Skewness2.4692251 × 10-6
Sum100540
Variance840.96009
MonotonicityNot monotonic
2024-12-18T18:52:19.703257image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 30
 
1.5%
2 28
 
1.4%
49 28
 
1.4%
46 28
 
1.4%
29 28
 
1.4%
65 27
 
1.3%
72 27
 
1.3%
80 27
 
1.3%
30 27
 
1.3%
71 27
 
1.3%
Other values (90) 1774
86.5%
ValueCountFrequency (%)
0 26
1.3%
1 24
1.2%
2 28
1.4%
3 25
1.2%
4 24
1.2%
5 13
0.6%
6 18
0.9%
7 11
 
0.5%
8 24
1.2%
9 23
1.1%
ValueCountFrequency (%)
99 14
0.7%
98 21
1.0%
97 19
0.9%
96 21
1.0%
95 17
0.8%
94 23
1.1%
93 20
1.0%
92 26
1.3%
91 17
0.8%
90 19
0.9%

MntWines
Real number (ℝ)

HIGH CORRELATION 

Distinct775
Distinct (%)37.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean304.02828
Minimum0
Maximum1493
Zeros12
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size96.6 KiB
2024-12-18T18:52:20.068737image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q123
median173
Q3504.5
95-th percentile1000.5
Maximum1493
Range1493
Interquartile range (IQR)481.5

Descriptive statistics

Standard deviation336.91524
Coefficient of variation (CV)1.1081707
Kurtosis0.57313126
Mean304.02828
Median Absolute Deviation (MAD)164
Skewness1.1738665
Sum623562
Variance113511.88
MonotonicityNot monotonic
2024-12-18T18:52:20.378553image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 39
 
1.9%
5 36
 
1.8%
6 34
 
1.7%
1 33
 
1.6%
4 31
 
1.5%
8 29
 
1.4%
3 28
 
1.4%
9 23
 
1.1%
12 23
 
1.1%
10 23
 
1.1%
Other values (765) 1752
85.4%
ValueCountFrequency (%)
0 12
 
0.6%
1 33
1.6%
2 39
1.9%
3 28
1.4%
4 31
1.5%
5 36
1.8%
6 34
1.7%
7 21
1.0%
8 29
1.4%
9 23
1.1%
ValueCountFrequency (%)
1493 1
< 0.1%
1492 1
< 0.1%
1486 1
< 0.1%
1478 1
< 0.1%
1462 1
< 0.1%
1459 1
< 0.1%
1449 1
< 0.1%
1396 1
< 0.1%
1394 1
< 0.1%
1379 1
< 0.1%

MntFruits
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct158
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.233057
Minimum0
Maximum199
Zeros367
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size96.6 KiB
2024-12-18T18:52:20.690161image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q333
95-th percentile123.5
Maximum199
Range199
Interquartile range (IQR)32

Descriptive statistics

Standard deviation39.757292
Coefficient of variation (CV)1.5155417
Kurtosis4.0757844
Mean26.233057
Median Absolute Deviation (MAD)8
Skewness2.1109526
Sum53804
Variance1580.6422
MonotonicityNot monotonic
2024-12-18T18:52:20.959421image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 367
 
17.9%
1 148
 
7.2%
2 113
 
5.5%
3 102
 
5.0%
4 93
 
4.5%
7 64
 
3.1%
5 62
 
3.0%
6 57
 
2.8%
8 44
 
2.1%
12 42
 
2.0%
Other values (148) 959
46.8%
ValueCountFrequency (%)
0 367
17.9%
1 148
7.2%
2 113
 
5.5%
3 102
 
5.0%
4 93
 
4.5%
5 62
 
3.0%
6 57
 
2.8%
7 64
 
3.1%
8 44
 
2.1%
9 32
 
1.6%
ValueCountFrequency (%)
199 1
 
< 0.1%
197 1
 
< 0.1%
194 2
0.1%
193 2
0.1%
190 1
 
< 0.1%
189 1
 
< 0.1%
185 2
0.1%
184 1
 
< 0.1%
183 3
0.1%
181 1
 
< 0.1%

MntMeatProducts
Real number (ℝ)

HIGH CORRELATION 

Distinct558
Distinct (%)27.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167.88493
Minimum0
Maximum1725
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size96.6 KiB
2024-12-18T18:52:21.209821image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q116
median67
Q3230
95-th percentile689
Maximum1725
Range1725
Interquartile range (IQR)214

Descriptive statistics

Standard deviation228.4785
Coefficient of variation (CV)1.3609232
Kurtosis5.6051612
Mean167.88493
Median Absolute Deviation (MAD)59
Skewness2.1035416
Sum344332
Variance52202.423
MonotonicityNot monotonic
2024-12-18T18:52:21.454607image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 46
 
2.2%
7 45
 
2.2%
11 44
 
2.1%
8 39
 
1.9%
6 38
 
1.9%
3 38
 
1.9%
9 37
 
1.8%
10 36
 
1.8%
16 34
 
1.7%
12 33
 
1.6%
Other values (548) 1661
81.0%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 14
 
0.7%
2 27
1.3%
3 38
1.9%
4 30
1.5%
5 46
2.2%
6 38
1.9%
7 45
2.2%
8 39
1.9%
9 37
1.8%
ValueCountFrequency (%)
1725 2
0.1%
1622 1
< 0.1%
1607 1
< 0.1%
1582 1
< 0.1%
984 1
< 0.1%
981 1
< 0.1%
974 1
< 0.1%
968 1
< 0.1%
961 1
< 0.1%
951 2
0.1%

MntFishProducts
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct182
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.241346
Minimum0
Maximum259
Zeros358
Zeros (%)17.5%
Negative0
Negative (%)0.0%
Memory size96.6 KiB
2024-12-18T18:52:21.695940image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median12
Q350
95-th percentile169
Maximum259
Range259
Interquartile range (IQR)47

Descriptive statistics

Standard deviation54.474785
Coefficient of variation (CV)1.4627502
Kurtosis3.1587057
Mean37.241346
Median Absolute Deviation (MAD)12
Skewness1.9332769
Sum76382
Variance2967.5022
MonotonicityNot monotonic
2024-12-18T18:52:21.941609image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 358
 
17.5%
2 144
 
7.0%
3 122
 
5.9%
4 96
 
4.7%
6 77
 
3.8%
7 61
 
3.0%
8 50
 
2.4%
10 49
 
2.4%
13 47
 
2.3%
11 43
 
2.1%
Other values (172) 1004
49.0%
ValueCountFrequency (%)
0 358
17.5%
1 10
 
0.5%
2 144
7.0%
3 122
 
5.9%
4 96
 
4.7%
5 1
 
< 0.1%
6 77
 
3.8%
7 61
 
3.0%
8 50
 
2.4%
10 49
 
2.4%
ValueCountFrequency (%)
259 1
 
< 0.1%
258 2
0.1%
254 1
 
< 0.1%
253 1
 
< 0.1%
250 3
0.1%
247 1
 
< 0.1%
246 1
 
< 0.1%
242 1
 
< 0.1%
240 2
0.1%
237 2
0.1%

MntSweetProducts
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct177
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.226719
Minimum0
Maximum263
Zeros385
Zeros (%)18.8%
Negative0
Negative (%)0.0%
Memory size96.6 KiB
2024-12-18T18:52:22.291365image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q334
95-th percentile128.5
Maximum263
Range263
Interquartile range (IQR)33

Descriptive statistics

Standard deviation41.759728
Coefficient of variation (CV)1.5337775
Kurtosis4.4047672
Mean27.226719
Median Absolute Deviation (MAD)8
Skewness2.147076
Sum55842
Variance1743.8749
MonotonicityNot monotonic
2024-12-18T18:52:22.683828image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 385
 
18.8%
1 150
 
7.3%
2 118
 
5.8%
3 92
 
4.5%
4 73
 
3.6%
6 59
 
2.9%
5 58
 
2.8%
7 52
 
2.5%
8 52
 
2.5%
12 43
 
2.1%
Other values (167) 969
47.2%
ValueCountFrequency (%)
0 385
18.8%
1 150
 
7.3%
2 118
 
5.8%
3 92
 
4.5%
4 73
 
3.6%
5 58
 
2.8%
6 59
 
2.9%
7 52
 
2.5%
8 52
 
2.5%
9 37
 
1.8%
ValueCountFrequency (%)
263 1
 
< 0.1%
262 1
 
< 0.1%
198 1
 
< 0.1%
197 1
 
< 0.1%
196 1
 
< 0.1%
195 1
 
< 0.1%
194 3
0.1%
192 3
0.1%
191 1
 
< 0.1%
189 2
0.1%

MntGoldProds
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct212
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.74354
Minimum0
Maximum362
Zeros57
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size96.6 KiB
2024-12-18T18:52:23.066042image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median24
Q356
95-th percentile163
Maximum362
Range362
Interquartile range (IQR)47

Descriptive statistics

Standard deviation51.954066
Coefficient of variation (CV)1.1876969
Kurtosis3.6811462
Mean43.74354
Median Absolute Deviation (MAD)19
Skewness1.9040538
Sum89718
Variance2699.2249
MonotonicityNot monotonic
2024-12-18T18:52:23.486115image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 68
 
3.3%
3 66
 
3.2%
4 63
 
3.1%
5 59
 
2.9%
12 58
 
2.8%
0 57
 
2.8%
2 57
 
2.8%
7 52
 
2.5%
6 49
 
2.4%
10 45
 
2.2%
Other values (202) 1477
72.0%
ValueCountFrequency (%)
0 57
2.8%
1 68
3.3%
2 57
2.8%
3 66
3.2%
4 63
3.1%
5 59
2.9%
6 49
2.4%
7 52
2.5%
8 36
1.8%
9 38
1.9%
ValueCountFrequency (%)
362 1
 
< 0.1%
321 1
 
< 0.1%
291 1
 
< 0.1%
262 1
 
< 0.1%
249 1
 
< 0.1%
248 1
 
< 0.1%
247 1
 
< 0.1%
246 1
 
< 0.1%
242 2
 
0.1%
241 5
0.2%

NumDealsPurchases
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3349586
Minimum0
Maximum15
Zeros42
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size96.6 KiB
2024-12-18T18:52:23.749964image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile6
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9429527
Coefficient of variation (CV)0.83211442
Kurtosis9.1808787
Mean2.3349586
Median Absolute Deviation (MAD)1
Skewness2.4502883
Sum4789
Variance3.7750651
MonotonicityNot monotonic
2024-12-18T18:52:24.012578image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 880
42.9%
2 458
22.3%
3 275
 
13.4%
4 178
 
8.7%
5 82
 
4.0%
6 58
 
2.8%
0 42
 
2.0%
7 34
 
1.7%
8 14
 
0.7%
15 7
 
0.3%
Other values (5) 23
 
1.1%
ValueCountFrequency (%)
0 42
 
2.0%
1 880
42.9%
2 458
22.3%
3 275
 
13.4%
4 178
 
8.7%
5 82
 
4.0%
6 58
 
2.8%
7 34
 
1.7%
8 14
 
0.7%
9 7
 
0.3%
ValueCountFrequency (%)
15 7
 
0.3%
13 3
 
0.1%
12 3
 
0.1%
11 5
 
0.2%
10 5
 
0.2%
9 7
 
0.3%
8 14
 
0.7%
7 34
1.7%
6 58
2.8%
5 82
4.0%

NumWebPurchases
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0950756
Minimum0
Maximum27
Zeros45
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size96.6 KiB
2024-12-18T18:52:24.283520image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q36
95-th percentile9
Maximum27
Range27
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.7956814
Coefficient of variation (CV)0.68269347
Kurtosis6.0801483
Mean4.0950756
Median Absolute Deviation (MAD)2
Skewness1.4302834
Sum8399
Variance7.8158343
MonotonicityNot monotonic
2024-12-18T18:52:24.538893image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2 343
16.7%
1 322
15.7%
3 308
15.0%
4 252
12.3%
5 202
9.8%
6 191
9.3%
7 142
6.9%
8 94
 
4.6%
9 66
 
3.2%
0 45
 
2.2%
Other values (5) 86
 
4.2%
ValueCountFrequency (%)
0 45
 
2.2%
1 322
15.7%
2 343
16.7%
3 308
15.0%
4 252
12.3%
5 202
9.8%
6 191
9.3%
7 142
6.9%
8 94
 
4.6%
9 66
 
3.2%
ValueCountFrequency (%)
27 2
 
0.1%
25 1
 
< 0.1%
23 1
 
< 0.1%
11 41
 
2.0%
10 41
 
2.0%
9 66
 
3.2%
8 94
4.6%
7 142
6.9%
6 191
9.3%
5 202
9.8%

NumCatalogPurchases
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6606533
Minimum0
Maximum28
Zeros536
Zeros (%)26.1%
Negative0
Negative (%)0.0%
Memory size96.6 KiB
2024-12-18T18:52:24.776284image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile8.5
Maximum28
Range28
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9323939
Coefficient of variation (CV)1.102133
Kurtosis8.6807208
Mean2.6606533
Median Absolute Deviation (MAD)2
Skewness1.953475
Sum5457
Variance8.598934
MonotonicityNot monotonic
2024-12-18T18:52:24.995701image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 536
26.1%
1 449
21.9%
2 259
12.6%
3 174
 
8.5%
4 168
 
8.2%
5 128
 
6.2%
6 110
 
5.4%
7 72
 
3.5%
8 52
 
2.5%
10 43
 
2.1%
Other values (4) 60
 
2.9%
ValueCountFrequency (%)
0 536
26.1%
1 449
21.9%
2 259
12.6%
3 174
 
8.5%
4 168
 
8.2%
5 128
 
6.2%
6 110
 
5.4%
7 72
 
3.5%
8 52
 
2.5%
9 38
 
1.9%
ValueCountFrequency (%)
28 3
 
0.1%
22 1
 
< 0.1%
11 18
 
0.9%
10 43
 
2.1%
9 38
 
1.9%
8 52
 
2.5%
7 72
3.5%
6 110
5.4%
5 128
6.2%
4 168
8.2%

NumStorePurchases
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7723062
Minimum0
Maximum13
Zeros15
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size96.6 KiB
2024-12-18T18:52:25.234683image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median5
Q38
95-th percentile12
Maximum13
Range13
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.2432043
Coefficient of variation (CV)0.5618559
Kurtosis-0.61157563
Mean5.7723062
Median Absolute Deviation (MAD)2
Skewness0.7005369
Sum11839
Variance10.518374
MonotonicityNot monotonic
2024-12-18T18:52:25.553414image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 448
21.8%
4 294
14.3%
2 206
10.0%
5 197
9.6%
6 160
 
7.8%
8 136
 
6.6%
7 132
 
6.4%
10 118
 
5.8%
9 99
 
4.8%
12 93
 
4.5%
Other values (4) 168
 
8.2%
ValueCountFrequency (%)
0 15
 
0.7%
1 7
 
0.3%
2 206
10.0%
3 448
21.8%
4 294
14.3%
5 197
9.6%
6 160
 
7.8%
7 132
 
6.4%
8 136
 
6.6%
9 99
 
4.8%
ValueCountFrequency (%)
13 75
 
3.7%
12 93
 
4.5%
11 71
 
3.5%
10 118
5.8%
9 99
 
4.8%
8 136
6.6%
7 132
6.4%
6 160
7.8%
5 197
9.6%
4 294
14.3%

NumWebVisitsMonth
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3193564
Minimum0
Maximum20
Zeros11
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size96.6 KiB
2024-12-18T18:52:25.875979image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q37
95-th percentile8
Maximum20
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.4388943
Coefficient of variation (CV)0.45849425
Kurtosis2.0175778
Mean5.3193564
Median Absolute Deviation (MAD)2
Skewness0.2562625
Sum10910
Variance5.9482056
MonotonicityNot monotonic
2024-12-18T18:52:26.262686image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
7 359
17.5%
6 310
15.1%
8 307
15.0%
5 259
12.6%
4 201
9.8%
3 191
9.3%
2 182
8.9%
1 139
 
6.8%
9 80
 
3.9%
0 11
 
0.5%
Other values (6) 12
 
0.6%
ValueCountFrequency (%)
0 11
 
0.5%
1 139
 
6.8%
2 182
8.9%
3 191
9.3%
4 201
9.8%
5 259
12.6%
6 310
15.1%
7 359
17.5%
8 307
15.0%
9 80
 
3.9%
ValueCountFrequency (%)
20 3
 
0.1%
19 2
 
0.1%
17 1
 
< 0.1%
14 2
 
0.1%
13 1
 
< 0.1%
10 3
 
0.1%
9 80
 
3.9%
8 307
15.0%
7 359
17.5%
6 310
15.1%

AcceptedCmp3
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size96.6 KiB
0
1901 
1
 
150

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2051
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1901
92.7%
1 150
 
7.3%

Length

2024-12-18T18:52:26.558591image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T18:52:26.779897image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1901
92.7%
1 150
 
7.3%

Most occurring characters

ValueCountFrequency (%)
0 1901
92.7%
1 150
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2051
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1901
92.7%
1 150
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2051
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1901
92.7%
1 150
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2051
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1901
92.7%
1 150
 
7.3%

AcceptedCmp4
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size96.6 KiB
0
1893 
1
 
158

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2051
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1893
92.3%
1 158
 
7.7%

Length

2024-12-18T18:52:27.013222image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T18:52:27.220536image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1893
92.3%
1 158
 
7.7%

Most occurring characters

ValueCountFrequency (%)
0 1893
92.3%
1 158
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2051
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1893
92.3%
1 158
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
Common 2051
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1893
92.3%
1 158
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2051
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1893
92.3%
1 158
 
7.7%

AcceptedCmp5
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size96.6 KiB
0
1904 
1
 
147

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2051
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1904
92.8%
1 147
 
7.2%

Length

2024-12-18T18:52:27.462837image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T18:52:27.678396image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1904
92.8%
1 147
 
7.2%

Most occurring characters

ValueCountFrequency (%)
0 1904
92.8%
1 147
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2051
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1904
92.8%
1 147
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
Common 2051
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1904
92.8%
1 147
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2051
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1904
92.8%
1 147
 
7.2%

AcceptedCmp1
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size96.6 KiB
0
1917 
1
 
134

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2051
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1917
93.5%
1 134
 
6.5%

Length

2024-12-18T18:52:27.888422image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T18:52:28.103940image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1917
93.5%
1 134
 
6.5%

Most occurring characters

ValueCountFrequency (%)
0 1917
93.5%
1 134
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2051
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1917
93.5%
1 134
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
Common 2051
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1917
93.5%
1 134
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2051
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1917
93.5%
1 134
 
6.5%

AcceptedCmp2
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size96.6 KiB
0
2025 
1
 
26

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2051
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2025
98.7%
1 26
 
1.3%

Length

2024-12-18T18:52:28.362193image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T18:52:28.568873image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2025
98.7%
1 26
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 2025
98.7%
1 26
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2051
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2025
98.7%
1 26
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2051
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2025
98.7%
1 26
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2051
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2025
98.7%
1 26
 
1.3%

Response
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size96.6 KiB
0
1741 
1
310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2051
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1741
84.9%
1 310
 
15.1%

Length

2024-12-18T18:52:28.868704image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T18:52:29.117256image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1741
84.9%
1 310
 
15.1%

Most occurring characters

ValueCountFrequency (%)
0 1741
84.9%
1 310
 
15.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2051
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1741
84.9%
1 310
 
15.1%

Most occurring scripts

ValueCountFrequency (%)
Common 2051
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1741
84.9%
1 310
 
15.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2051
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1741
84.9%
1 310
 
15.1%

Age
Real number (ℝ)

Distinct59
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.229157
Minimum28
Maximum131
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.6 KiB
2024-12-18T18:52:29.488270image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile36.5
Q147
median54
Q365
95-th percentile74
Maximum131
Range103
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.968583
Coefficient of variation (CV)0.21670769
Kurtosis0.85806751
Mean55.229157
Median Absolute Deviation (MAD)9
Skewness0.37915785
Sum113275
Variance143.24697
MonotonicityNot monotonic
2024-12-18T18:52:29.806790image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48 81
 
3.9%
53 79
 
3.9%
49 77
 
3.8%
54 73
 
3.6%
52 73
 
3.6%
46 71
 
3.5%
59 66
 
3.2%
55 65
 
3.2%
51 65
 
3.2%
50 63
 
3.1%
Other values (49) 1338
65.2%
ValueCountFrequency (%)
28 2
 
0.1%
29 4
 
0.2%
30 3
 
0.1%
31 3
 
0.1%
32 12
0.6%
33 14
0.7%
34 16
0.8%
35 25
1.2%
36 24
1.2%
37 26
1.3%
ValueCountFrequency (%)
131 1
 
< 0.1%
125 1
 
< 0.1%
124 1
 
< 0.1%
84 1
 
< 0.1%
83 1
 
< 0.1%
81 6
 
0.3%
80 7
0.3%
79 8
0.4%
78 11
0.5%
77 15
0.7%

Years_Since_Join
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size96.6 KiB
11
1088 
10
514 
12
449 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters4102
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row12
2nd row10
3rd row11
4th row10
5th row10

Common Values

ValueCountFrequency (%)
11 1088
53.0%
10 514
25.1%
12 449
21.9%

Length

2024-12-18T18:52:30.112650image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T18:52:30.358565image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
11 1088
53.0%
10 514
25.1%
12 449
21.9%

Most occurring characters

ValueCountFrequency (%)
1 3139
76.5%
0 514
 
12.5%
2 449
 
10.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4102
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3139
76.5%
0 514
 
12.5%
2 449
 
10.9%

Most occurring scripts

ValueCountFrequency (%)
Common 4102
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3139
76.5%
0 514
 
12.5%
2 449
 
10.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4102
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3139
76.5%
0 514
 
12.5%
2 449
 
10.9%

Total_Amnt_Spend
Real number (ℝ)

HIGH CORRELATION 

Distinct1052
Distinct (%)51.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean606.35787
Minimum5
Maximum2525
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.6 KiB
2024-12-18T18:52:30.701975image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile22
Q168.5
median396
Q31046
95-th percentile1780.5
Maximum2525
Range2520
Interquartile range (IQR)977.5

Descriptive statistics

Standard deviation603.32768
Coefficient of variation (CV)0.99500262
Kurtosis-0.32716592
Mean606.35787
Median Absolute Deviation (MAD)353
Skewness0.86739062
Sum1243640
Variance364004.28
MonotonicityNot monotonic
2024-12-18T18:52:31.024589image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46 16
 
0.8%
57 15
 
0.7%
22 15
 
0.7%
55 15
 
0.7%
20 14
 
0.7%
44 13
 
0.6%
17 13
 
0.6%
38 13
 
0.6%
48 13
 
0.6%
43 12
 
0.6%
Other values (1042) 1912
93.2%
ValueCountFrequency (%)
5 1
 
< 0.1%
6 2
 
0.1%
8 4
0.2%
9 2
 
0.1%
10 5
0.2%
11 5
0.2%
12 2
 
0.1%
13 6
0.3%
14 3
 
0.1%
15 9
0.4%
ValueCountFrequency (%)
2525 2
0.1%
2524 1
< 0.1%
2486 1
< 0.1%
2440 1
< 0.1%
2352 1
< 0.1%
2349 1
< 0.1%
2346 1
< 0.1%
2302 1
< 0.1%
2283 1
< 0.1%
2279 1
< 0.1%

No_of_Childrens
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size96.6 KiB
1
1042 
0
575 
2
386 
3
 
48

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2051
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row2
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1042
50.8%
0 575
28.0%
2 386
 
18.8%
3 48
 
2.3%

Length

2024-12-18T18:52:31.307483image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T18:52:31.556084image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1042
50.8%
0 575
28.0%
2 386
 
18.8%
3 48
 
2.3%

Most occurring characters

ValueCountFrequency (%)
1 1042
50.8%
0 575
28.0%
2 386
 
18.8%
3 48
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2051
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1042
50.8%
0 575
28.0%
2 386
 
18.8%
3 48
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2051
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1042
50.8%
0 575
28.0%
2 386
 
18.8%
3 48
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2051
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1042
50.8%
0 575
28.0%
2 386
 
18.8%
3 48
 
2.3%

Is_Parent
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size96.6 KiB
1
1476 
0
575 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2051
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1476
72.0%
0 575
 
28.0%

Length

2024-12-18T18:52:31.923216image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T18:52:32.208189image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1476
72.0%
0 575
 
28.0%

Most occurring characters

ValueCountFrequency (%)
1 1476
72.0%
0 575
 
28.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2051
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1476
72.0%
0 575
 
28.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2051
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1476
72.0%
0 575
 
28.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2051
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1476
72.0%
0 575
 
28.0%

Memebers_In_Family
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size96.6 KiB
3
816 
2
696 
4
278 
1
233 
5
 
28

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2051
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 816
39.8%
2 696
33.9%
4 278
 
13.6%
1 233
 
11.4%
5 28
 
1.4%

Length

2024-12-18T18:52:32.545486image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T18:52:32.824721image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
3 816
39.8%
2 696
33.9%
4 278
 
13.6%
1 233
 
11.4%
5 28
 
1.4%

Most occurring characters

ValueCountFrequency (%)
3 816
39.8%
2 696
33.9%
4 278
 
13.6%
1 233
 
11.4%
5 28
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2051
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 816
39.8%
2 696
33.9%
4 278
 
13.6%
1 233
 
11.4%
5 28
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common 2051
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 816
39.8%
2 696
33.9%
4 278
 
13.6%
1 233
 
11.4%
5 28
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2051
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 816
39.8%
2 696
33.9%
4 278
 
13.6%
1 233
 
11.4%
5 28
 
1.4%

Total_Purchase
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.18235
Minimum0
Maximum46
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size96.6 KiB
2024-12-18T18:52:33.126631image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q114
median19
Q325
95-th percentile33
Maximum46
Range46
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.3017806
Coefficient of variation (CV)0.36179041
Kurtosis-0.476966
Mean20.18235
Median Absolute Deviation (MAD)6
Skewness0.40591007
Sum41394
Variance53.316001
MonotonicityNot monotonic
2024-12-18T18:52:33.449981image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
12 124
 
6.0%
14 123
 
6.0%
13 113
 
5.5%
24 104
 
5.1%
11 96
 
4.7%
15 96
 
4.7%
16 95
 
4.6%
18 92
 
4.5%
23 90
 
4.4%
25 89
 
4.3%
Other values (32) 1029
50.2%
ValueCountFrequency (%)
0 2
 
0.1%
1 1
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
6 1
 
< 0.1%
7 6
 
0.3%
8 14
 
0.7%
9 33
 
1.6%
10 59
2.9%
11 96
4.7%
ValueCountFrequency (%)
46 1
 
< 0.1%
44 2
 
0.1%
41 2
 
0.1%
40 2
 
0.1%
39 8
 
0.4%
38 12
0.6%
37 12
0.6%
36 9
 
0.4%
35 17
0.8%
34 26
1.3%

Interactions

2024-12-18T18:52:13.510763image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:23.757770image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:27.806959image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:31.992568image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:35.301725image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:38.469904image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:41.581017image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:44.596187image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:48.624595image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:51.738262image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:54.826552image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:57.771031image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:00.723789image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:03.731936image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:07.703810image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:10.533144image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:13.694371image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:24.056394image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:27.981966image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:32.254574image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:35.480338image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:38.661328image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:41.750192image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:44.762641image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:48.793849image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:51.902920image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:54.990235image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:57.932496image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:00.888606image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:03.910606image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:07.924471image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:10.763395image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:13.923175image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:24.285766image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:29.449862image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:32.424996image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:35.655491image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:38.846585image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:41.905361image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:44.960952image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:48.967203image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:52.078188image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:55.153594image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:58.098113image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:01.049657image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:04.085200image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:08.127223image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:10.983323image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:14.130468image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:24.508855image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:29.655236image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:32.609294image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:35.838866image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:39.047213image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:42.095520image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:45.154760image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:49.169960image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:52.249771image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:55.338608image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:58.266371image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:01.221778image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:04.266397image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:08.365100image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:11.210227image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:14.334091image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:24.746247image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:29.833341image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:32.791101image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:36.027257image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:39.234987image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:42.276032image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:45.342897image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:49.416143image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:52.432765image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:55.543594image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:58.460987image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:01.456454image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:04.509717image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:08.532547image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:11.395727image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:14.527673image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:24.983956image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:30.009203image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:32.965738image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:36.219518image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:39.418274image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:42.445212image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:46.352486image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:49.653890image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:52.678515image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:55.773807image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:58.717504image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:01.742070image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:04.744595image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:08.704551image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:11.563496image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:14.687468image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:25.280191image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:30.175464image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:33.144188image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:36.383837image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:39.582558image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:42.599007image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:46.610329image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:49.896664image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:52.917594image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:55.980231image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:58.900109image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:01.967783image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:04.960428image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:08.855444image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:11.730151image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:14.841801image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:25.660256image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:30.336641image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:33.314257image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:36.564541image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:39.745311image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:42.759703image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:46.862977image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:50.144799image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:53.162460image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:56.247381image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:59.110956image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:02.191086image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:05.219173image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:09.025843image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:11.909777image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:15.018337image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:25.966019image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:30.526407image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:33.488687image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:36.770472image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:39.923626image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:42.944861image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:47.212664image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:50.375176image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:53.439271image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:56.455567image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:59.383598image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:02.400741image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:05.458086image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:09.192710image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:12.085543image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:15.218732image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:26.295666image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:30.697522image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:33.728850image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:36.946804image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:40.106786image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:43.116620image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:47.458124image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:50.560807image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:53.611565image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:56.624070image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:59.560003image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:02.568261image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:05.659786image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:09.355057image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:12.260092image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:15.365021image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:26.524911image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:30.865355image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:33.907269image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:37.124972image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:40.269341image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:43.292647image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:47.637350image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:50.721388image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:53.777567image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:56.774745image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:59.732419image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:02.730772image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:05.829736image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:09.511948image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:12.426503image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:15.528803image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:26.731874image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:31.027659image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:34.111659image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:37.290949image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:40.503454image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:43.528938image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:47.812082image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:50.880920image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:53.942423image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:56.928959image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:59.900672image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:02.888337image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:06.001411image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:09.672371image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:12.599096image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:15.683671image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:26.962384image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:31.193235image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:34.279285image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:37.523407image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:40.722563image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:43.747661image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:47.977643image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:51.058348image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:54.119276image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:57.096392image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:00.075953image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:03.056323image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:06.181503image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:09.850483image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:12.767346image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:15.892126image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:27.233786image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:31.378567image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:34.619551image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:37.811638image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:41.020049image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:44.020938image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:48.161332image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:51.252589image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:54.340981image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:57.273714image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:00.254337image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:03.244165image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:07.105799image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:10.034793image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:12.944220image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:16.051638image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:27.436293image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:31.592621image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:34.836735image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:38.080108image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:41.233775image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:44.285995image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:48.312200image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:51.408088image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:54.496853image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:57.449691image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:00.411970image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:03.395717image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:07.271651image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:10.184277image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:13.099272image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:16.224881image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:27.634984image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:31.791392image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:35.066815image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:38.306378image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:41.423714image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:44.446352image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:48.470553image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:51.581666image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:54.668195image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:51:57.619129image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:00.571751image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:03.572619image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:07.464553image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:10.338319image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-18T18:52:13.269133image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-12-18T18:52:33.741081image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
AcceptedCmp1AcceptedCmp2AcceptedCmp3AcceptedCmp4AcceptedCmp5AgeEducationIncomeIs_ParentMarital_StatusMemebers_In_FamilyMntFishProductsMntFruitsMntGoldProdsMntMeatProductsMntSweetProductsMntWinesNo_of_ChildrensNumCatalogPurchasesNumDealsPurchasesNumStorePurchasesNumWebPurchasesNumWebVisitsMonthRecencyResponseTotal_Amnt_SpendTotal_PurchaseYears_Since_Join
AcceptedCmp11.0000.1710.0940.2450.396-0.0070.0280.3280.2840.0210.1980.2100.1800.1740.2900.2320.3080.2860.302-0.1750.2100.184-0.194-0.0250.2870.3270.1870.025
AcceptedCmp20.1711.0000.0560.2690.2120.0060.0000.1040.0870.0230.0760.0030.0100.0740.0690.0030.1360.0870.109-0.0510.0870.041-0.010-0.0100.1760.1220.0830.000
AcceptedCmp30.0940.0561.0000.0740.075-0.0720.000-0.0150.0000.0000.000-0.0310.0150.1480.012-0.0210.0390.0000.103-0.014-0.0790.0400.065-0.0340.2440.0410.0420.000
AcceptedCmp40.2450.2690.0741.0000.2910.0700.0460.2170.0750.0000.0660.0000.0280.0630.1360.0050.3080.0820.1850.0050.2020.183-0.0250.0230.1650.2400.2100.027
AcceptedCmp50.3960.2120.0750.2911.000-0.0160.0330.3900.3500.0000.2450.2100.2320.1890.3280.2520.3610.3510.320-0.2530.2260.173-0.269-0.0040.3170.3780.1560.000
Age-0.0070.006-0.0720.070-0.0161.0000.1160.2210.2550.0410.1320.0330.0310.0760.116-0.0040.2400.1770.1820.0780.1730.163-0.1360.0200.0000.1610.1560.000
Education0.0280.0000.0000.0460.0330.1161.0000.1380.0000.0000.032-0.170-0.163-0.1320.062-0.1720.2290.0320.0850.0200.0820.086-0.058-0.0080.0820.0930.0850.047
Income0.3280.104-0.0150.2170.3900.2210.1381.0000.5260.0000.2280.5730.5740.4970.8090.5640.8230.3050.786-0.1970.7240.562-0.6370.0020.1910.8440.5550.000
Is_Parent0.2840.0870.0000.0750.3500.2550.0000.5261.0000.0320.760-0.464-0.439-0.275-0.492-0.429-0.3311.000-0.4480.539-0.309-0.1250.4660.0140.213-0.472-0.1170.000
Marital_Status0.0210.0230.0000.0000.0000.0410.0000.0000.0321.0000.3580.0170.0190.0210.0130.0090.0070.0370.011-0.038-0.023-0.013-0.0200.0260.0740.007-0.0180.000
Memebers_In_Family0.1980.0760.0000.0660.2450.1320.0320.2280.7600.3581.000-0.402-0.396-0.278-0.404-0.377-0.2780.755-0.3800.442-0.273-0.1600.3460.0210.263-0.400-0.1280.011
MntFishProducts0.2100.003-0.0310.0000.2100.033-0.1700.573-0.4640.017-0.4021.0000.7070.5610.7270.7050.5230.2820.653-0.1100.5840.470-0.4460.0110.1200.6940.4800.079
MntFruits0.1800.0100.0150.0280.2320.031-0.1630.574-0.4390.019-0.3960.7071.0000.5680.7130.6900.5140.2670.631-0.1020.5790.468-0.4310.0180.1520.6800.4750.075
MntGoldProds0.1740.0740.1480.0630.1890.076-0.1320.497-0.2750.021-0.2780.5610.5681.0000.6360.5430.5750.1580.6490.1010.5370.583-0.2450.0130.1480.6920.5930.110
MntMeatProducts0.2900.0690.0120.1360.3280.1160.0620.809-0.4920.013-0.4040.7270.7130.6361.0000.6980.8210.3500.850-0.0300.7760.675-0.4840.0230.2480.9390.7130.019
MntSweetProducts0.2320.003-0.0210.0050.252-0.004-0.1720.564-0.4290.009-0.3770.7050.6900.5430.6981.0000.5000.2470.624-0.1020.5800.460-0.4410.0250.1090.6690.4730.049
MntWines0.3080.1360.0390.3080.3610.2400.2290.823-0.3310.007-0.2780.5230.5140.5750.8210.5001.0000.2220.8230.0590.8040.736-0.3820.0150.2570.9260.7690.131
No_of_Childrens0.2860.0870.0000.0820.3510.1770.0320.3051.0000.0370.7550.2820.2670.1580.3500.2470.2221.000-0.4620.534-0.347-0.1970.4280.0250.214-0.489-0.1600.010
NumCatalogPurchases0.3020.1090.1030.1850.3200.1820.0850.786-0.4480.011-0.3800.6530.6310.6490.8500.6240.823-0.4621.000-0.0300.7090.618-0.5280.0210.2160.8920.7150.057
NumDealsPurchases-0.175-0.051-0.0140.005-0.2530.0780.020-0.1970.539-0.0380.442-0.110-0.1020.101-0.030-0.1020.0590.534-0.0301.0000.0990.2810.3970.0190.105-0.0110.4060.140
NumStorePurchases0.2100.087-0.0790.2020.2260.1730.0820.724-0.309-0.023-0.2730.5840.5790.5370.7760.5800.804-0.3470.7090.0991.0000.665-0.4480.0070.1420.8010.7670.075
NumWebPurchases0.1840.0410.0400.1830.1730.1630.0860.562-0.125-0.013-0.1600.4700.4680.5830.6750.4600.736-0.1970.6180.2810.6651.000-0.089-0.0040.1640.7250.8600.126
NumWebVisitsMonth-0.194-0.0100.065-0.025-0.269-0.136-0.058-0.6370.466-0.0200.346-0.446-0.431-0.245-0.484-0.441-0.3820.428-0.5280.397-0.448-0.0891.000-0.0160.121-0.468-0.0320.200
Recency-0.025-0.010-0.0340.023-0.0040.020-0.0080.0020.0140.0260.0210.0110.0180.0130.0230.0250.0150.0250.0210.0190.007-0.004-0.0161.0000.2080.0140.0010.000
Response0.2870.1760.2440.1650.3170.0000.0820.1910.2130.0740.2630.1200.1520.1480.2480.1090.2570.2140.2160.1050.1420.1640.1210.2081.0000.2390.1710.181
Total_Amnt_Spend0.3270.1220.0410.2400.3780.1610.0930.844-0.4720.007-0.4000.6940.6800.6920.9390.6690.926-0.4890.892-0.0110.8010.725-0.4680.0140.2391.0000.7700.118
Total_Purchase0.1870.0830.0420.2100.1560.1560.0850.555-0.117-0.018-0.1280.4800.4750.5930.7130.4730.769-0.1600.7150.4060.7670.860-0.0320.0010.1710.7701.0000.190
Years_Since_Join0.0250.0000.0000.0270.0000.0000.0470.0000.0000.0000.0110.0790.0750.1100.0190.0490.1310.0100.0570.1400.0750.1260.2000.0000.1810.1180.1901.000

Missing values

2024-12-18T18:52:16.625684image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-18T18:52:17.461442image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

EducationMarital_StatusIncomeRecencyMntWinesMntFruitsMntMeatProductsMntFishProductsMntSweetProductsMntGoldProdsNumDealsPurchasesNumWebPurchasesNumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ResponseAgeYears_Since_JoinTotal_Amnt_SpendNo_of_ChildrensIs_ParentMemebers_In_FamilyTotal_Purchase
0GraduationSingle58138.0586358854617288883810470000016712161700132
1GraduationSingle46344.03811162162112500000070102721311
2GraduationSingle71613.026426491271112142182104000000591177600225
3GraduationSingle26646.0261142010352204600000040105311314
4PhDMarried58293.0941734311846271555365000000431042211324
5MasterSingle62513.016520429804214264106000000571171611328
6GraduationSingle55635.0342356516450492747376000000531259011227
7PhDMarried33454.032761056312324048000000391116911318
8PhDSingle30351.019140243321302900000150114611315
9PhDSingle5648.0682806111311002010000074104921422
EducationMarital_StatusIncomeRecencyMntWinesMntFruitsMntMeatProductsMntFishProductsMntSweetProductsMntGoldProdsNumDealsPurchasesNumWebPurchasesNumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ResponseAgeYears_Since_JoinTotal_Amnt_SpendNo_of_ChildrensIs_ParentMemebers_In_FamilyTotal_Purchase
22282n CycleSingle52357.7915445332216071242200010010000461216790021
2229GraduationMarried24434.0000009328200172212700000052105021414
2230GraduationSingle11012.000000822432671233312910000040118411218
2231MasterSingle44802.00000071853101431310202941280000005412104900135
2232GraduationSingle26816.00000050516343100340000003812220018
2233GraduationSingle666666.000000239141881124313600000047116211317
2235GraduationMarried61223.000000467094318242118247293450000005711134111323
2237GraduationSingle56981.00000091908482173212241231360100004310124100125
2238MasterSingle69245.000000842830214803061265103000000681084311326
2239PhDMarried52869.0000004084361212133147000001701217221418